Are there Structural Breaks in Realized Volatility?

摘要

Constructed from high-frequency data, realized volatility (RV) provides an efficient estimate of the unobserved volatility of financial markets. This paper uses a Bayesian approach to investigate the evidence for structural breaks in reduced form time-series models of RV. We focus on the popular heterogeneous autoregressive (HAR) models of the logarithm of realized volatility. Using Monte Carlo simulations we demonstrate that our estimation approach is effictive in identifying and dating structural breaks. Applied to daily S&P 500 data, we find strong evidence of a single structural break in log(RV ). The main effect of the break is on the long-run mean and variance of log-volatility.